Literature DB >> 20527543

MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration.

Eduard Schreibmann1, Jonathon A Nye, David M Schuster, Diego R Martin, John Votaw, Tim Fox.   

Abstract

PURPOSE: Realization of combined positron emission tomography (PET)--magnetic resonance (MR) scanners has the potential to significantly change healthcare and revolutionize clinical practice as it allows, simultaneously, visualization of molecular imaging and anatomical imaging. PET-MR, acquired in one imaging study, will likely become the advanced imaging modality of choice for neurological studies, certain forms of cancer, stroke, and the emerging study of stem cell therapy. A challenge toward the implementation and operation of combined PET-MR scanners is that attenuation corrections maps are not directly available due to space and cost constraints. This article presents a method to obtain accurate patient-specific PET attenuation coefficients maps in head imaging by warping an atlas computed tomography (CT) data set to the patient-specific MR data set using a deformable registration model.
METHODS: A multimodality optical flow deformable model has been developed that establishes a voxel-to-voxel correspondence between the CT atlas and patient MR images. Once the mapping is established, the atlas is warped with the deformation field obtained by the registration to create a simulated CT image study that matches the patient anatomy, which could be used for attenuation correction.
RESULTS: To evaluate the accuracy of the deformable-based attenuation correction, 17 clinical brain tumor cases were studied using acquired MR-CT images. A simulated CT was compared to the patient's true CT to assess geometrical accuracy of the deformation module as well as voxel-to-voxel comparison of Hounsfield units (HUs). In all cases, mapping from the atlas CT to the individual MR was achieved with geometrical accuracy as judged using quantitative inspection tools. The mean distance between simulated and true CT external contour and bony anatomy was 1.26 and 2.15 mm, respectively. In terms of HU unit comparison, the mean voxel-to-voxel difference was less than 2 HU for all cases.
CONCLUSIONS: Attenuation correction for hybrid PET-MR scanners was easily achieved by individualizing an atlas CT to the MR data set using a deformable model without requiring user interaction. The method provided clinical accuracy while eliminating the need for an additional CT scan for PET attenuation correction.

Entities:  

Mesh:

Year:  2010        PMID: 20527543     DOI: 10.1118/1.3377774

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  32 in total

1.  PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction.

Authors:  Lars Birger Aasheim; Anna Karlberg; Pål Erik Goa; Asta Håberg; Sveinung Sørhaug; Unn-Merete Fagerli; Live Eikenes
Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-04-22       Impact factor: 9.236

Review 2.  From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies.

Authors:  Zhaolin Chen; Sharna D Jamadar; Shenpeng Li; Francesco Sforazzini; Jakub Baran; Nicholas Ferris; Nadim Jon Shah; Gary F Egan
Journal:  Hum Brain Mapp       Date:  2018-08-04       Impact factor: 5.038

3.  Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.

Authors:  Yang Lei; Hui-Kuo Shu; Sibo Tian; Jiwoong Jason Jeong; Tian Liu; Hyunsuk Shim; Hui Mao; Tonghe Wang; Ashesh B Jani; Walter J Curran; Xiaofeng Yang
Journal:  J Med Imaging (Bellingham)       Date:  2018-08-24

Review 4.  Sequential whole-body PET/MR scanner: concept, clinical use, and optimisation after two years in the clinic. The manufacturer's perspective.

Authors:  Antonis Kalemis; Bénédicte M A Delattre; Susanne Heinzer
Journal:  MAGMA       Date:  2012-08-07       Impact factor: 2.310

5.  One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-06-03       Impact factor: 9.236

6.  Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging.

Authors:  Xiangrui Zeng; Min Xu
Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit       Date:  2020-08-05

7.  Pseudo CT Estimation from MRI Using Patch-based Random Forest.

Authors:  Xiaofeng Yang; Yang Lei; Hui-Kuo Shu; Peter Rossi; Hui Mao; Hyunsuk Shim; Walter J Curran; Tian Liu
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-02

Review 8.  MR Imaging-Guided Attenuation Correction of PET Data in PET/MR Imaging.

Authors:  David Izquierdo-Garcia; Ciprian Catana
Journal:  PET Clin       Date:  2016-01-26

Review 9.  Morphology supporting function: attenuation correction for SPECT/CT, PET/CT, and PET/MR imaging.

Authors:  Tzu C Lee; Adam M Alessio; Robert M Miyaoka; Paul E Kinahan
Journal:  Q J Nucl Med Mol Imaging       Date:  2015-11-17       Impact factor: 2.346

10.  Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging.

Authors:  Hyungseok Jang; Fang Liu; Gengyan Zhao; Tyler Bradshaw; Alan B McMillan
Journal:  Med Phys       Date:  2018-05-15       Impact factor: 4.071

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